MICDE’s InfoReady portal logging in issue

By | Uncategorized

We are working on resolving a bug when logging in into the MICDE InfoReady portal using U-M’s SSO. While we fix it, if you are getting the following screen:

please follow these steps:

  • Go to https://micde.infoready4.com/#login.
  • Scroll down to the Login for Other Users, and click Forgot your password.
  • Enter your umich email address and then click Reset Password. An email will be sent to that address with instructions for resetting your password.
  • Follow the instructions, and you will be redirected to the site and logged in automatically.

You can use the Login for Other Users instead of the Single Sign On as long as necessary; the password will not change unless you change it in your User Profile.

We apologize for any inconvenience.

Professor Karthik Duraisamy named new director of the Ph.D. in Scientific Computing program

By | Educational, Feature
Prof. Karthik Duraisamy infront of screen with turbulence simulation

Professor Karthik Duraisamy (Aerospace Engineering)

Karthik Duraisamy, associate professor of Aerospace Engineering, and an associate director of the Michigan Institute for Computational Discovery & Engineering, has been named director of the joint Ph.D. in Scientific Computing program effective on January 1, 2022. Professor Duraisamy’s research involves the development of theory and algorithms for computational modeling of complex physical systems. He was the principal investigador of ConFlux, an NSF Major Research Instrumentation project that led to the development of a first of its kind computing instrument specifically designed to enable High Performance Computing (HPC) clusters to communicate seamlessly and at interactive speeds with data-intensive operations. Currently he directs the Air Force Center of Excellence on Rocket Combustion modeling. He is invested in educating future researchers with a strong computational background capable of using the power of computing for problem solving. He worked with the group that launched the course Methods and Practice in Scientific Computing, and developed and teaches a course on data-driven analysis and modeling of complex systems. These two courses give students a solid foundation, enabling them to use HPC in their research. 

Portrait of Ken Powell

Professor Ken Powell (Aerospace Engineering)

Professor Duraisamy replaces Ken Powell, Arthur F. Thurnau Professor of Aerospace Engineering, who stepped down from the role after 18 years of service. As a young assistant professor, Professor Powell was an instrumental member of the original team that conceived and launched the program back in 1989. The field of computational fluid dynamics, where his research interests lie, has always included an active community of HPC users and developers, thus he was always actively involved in the program through research, teaching and student advising. In 2004 he succeeded Professor William Martin as director of the program. During his time as director, he met and advised every single one of the over 350 students that enrolled in the program. Through this time he became an expert on scientific computing courses across the university, and witnessed first hand the explosion in computational and data science usage, reflected in the research scope of the students enrolling in the program.

Professor Duraisamy has big shoes to fill, but he is being assisted by the MICDE Management and Education Committee. The program’s mission, to train U-M students in scientific computing and to support the growing computational and data science community at the University of Michigan, will itself continue to expand.

The University of Michigan Ph.D. in Scientific Computing timeline. Read more.

 

Job Opening: Physics-AI Hybrid Modeling Research Engineer at Bosch

By | Feature, SC2 jobs

The Bosch Research and Technology Center in Sunnyvale, CA seeks to hire an outstanding research engineer to develop novel hybrid multiscale, cross-domain modeling and simulation tools for Bosch products. This engineer would join a team of PhDs with a variety of competences including high fidelity CFD-based multiphysics modeling, adjoint-based optimization, machine learning and high performance computing. The team focuses on design and optimization of novel clean and sustainable energy solutions such as fuel cells and electric vehicle components.

Primary responsibilities:

  • Build models which utilize machine learning and hybrid modeling approaches to capture complex physical phenomena and accelerate solution time of physical models
  • Develop multiscale models together with materials and systems modelers
  • Develop hybrid performance and aging models for Bosch products including polymer electrolyte fuel cells
  • Integrate hybrid performance and aging models into system simulation
  • Collaborate with experimentalists, top universities and our partners in Silicon Valley

Read more.

 

Reminder: seeking nominations for MICDE graduate fellowships 2022-2023 

By | Educational, Funding Opportunities
MICDE is currently accepting nominations of prospective students for an MICDE graduate fellowship. If you are an U-M faculty, and have identified an incoming graduate student interested in research in Computational Discovery and Engineering with your research group, we invite you to nominate them for the award. MICDE seeks to recruit and retain a diverse cohort of students to contribute to building a more diversified, equitable and inclusive STEM community. Please keep this is mind when sending your nomination. Nominations will be reviewed on a rolling basis. We will accept nominations until we reach our funding capacity, but no later than Friday, March 11, 2022.

New physics-based computation and AI framework to understand the agressive behavior of cancer cells

By | Feature, Research

Cancer is an illness caused by an uncontrolled division of transformed cells, which can originate in almost  any organ of the body.  Cancer is not a single disease, even when it arises in the same site of the body. Tremendous variability exists in progression of disease and response to therapy among different persons with the same general type of cancer, such as breast cancer. Even at the level of a single person, cancer cells show tremendous heterogeneity within a single tumor and among a primary tumor and metastases. This heterogeneity causes drug resistance and fatal disease. The prevailing dogma is that heterogeneity among cancer cells arises randomly, generating greedy individual cancer cells that compete for growth factors and optimal environments. The rare “winners” in this competition survive and metastasize. However, tumors consistently maintain heterogeneous subpopulations of cancer cells, some of which appear less able to grow and spread. This observation prompted Gary and Kathy Luker, cancer cell biologists at the University of Michigan, to hypothesize that cancer cells may actually collaborate under some circumstances to cause disease and not just compete. The idea that single, heterogeneous cancer cells work collectively within a constrained range of variability to drive population-level outputs in tumor progression is a ground-breaking concept that may revolutionize how we approach cancer biology and therapy.

The team is using innovative approaches to extract and merge data streams from models that generate heterogeneous cell behaviors

...cancer cell biologists have teamed up with computational scientists and experts in artificial intelligence to focus the power of these fields on understanding and overcoming heterogeneity in cancer.

To understand causes of single-cell heterogeneity in cancer and conditions that motivate cancer cells to collaborate, an interdisciplinary team of scientists at UM formulated an entirely new conceptual approach to this challenging problem. The cancer cell biologists have teamed up with computational scientists and experts in artificial intelligence to focus the power of these fields on understanding and overcoming heterogeneity in cancer. Building on large, single-cell data sets unique to the team, they will combine inverse reinforcement learning, an artificial intelligence method typically applied to discover motivations for human behaviors, with computational models inferred on the basis of the physics and chemistry of cell signaling and migration. They have proposed an entirely new conceptual approach combining single cell data, physics-based modeling and artificial intelligence to single-cell heterogeneity and intercellular interactions. By discovering  testable molecular processes underlying “decision-making” by single cells and their “motivations” for acting competitively or collaboratively, this research blazes a new path to understand and treat cancer. Their high-risk, high-reward approach to understand how each cell in a population processes information and translates that to action driving cancer progression, has attracted an award of $1 million dollars by the Keck Foundation. 

The team includes Gary Luker (Radiology, Microbiology and Immunology; Biomedical Engineering), and Kathryn Luker (Radiology), who are leading the experimental studies of cell signaling and migration; Jennifer Linderman (Chemical Engineering; Biomedical Engineering); and Krishna Garikipati (Mechanical Engineering; Mathematics), who are leading the machine learning and modeling side of the project. Nikola Banovic (Electrical Engineering and Computer Science) and Xun Huan (Mechanical Engineering) are using artificial intelligence approaches to discover decision-making policies and rewards for cancer cells, working with the rest of the investigators to incorporate experimental data and physics/chemistry-based models into their approaches.

The W. M. Keck Foundation was established in 1954 in Los Angeles by William Myron Keck, founder of The Superior Oil Company. One of the nation’s largest philanthropic organizations, the W. M. Keck Foundation supports outstanding science, engineering and medical research. The Foundation also supports undergraduate education and maintains a program within Southern California to support arts and culture, education, health and community service projects. This project incorporates elements from all the W. M. Keck Foundation’s focus research areas to tackle cancer with a novel, physics-based modeling and AI-centered approach.  The idea for this project originated in the 2020 MICDE faculty workshop in AI for Physically based Bio-medicine Workshop. The workshop brought together an interdisciplinary group of faculty members to discuss ways to advance artificial intelligence and machine learning methods for biomedical problems. After seeding the idea, a subset of these researchers were awarded an MICDE catalyst grant and a MIDAS PODS grant. These funds were used to establish the proof of concept and to generate preliminary results. 

Computational science is becoming increasingly indispensable in many areas of biomedical science. While the current proposal focuses on cancer, this innovative computational framework represents a transformative leap with widespread applications in multiple other biomedical, physical, and social sciences. MICDE supports innovative and interdisciplinary projects aiming to advance the current paradigms.

Portraits of Kathryn Luker, Gary Luker, Krishna Garikipati, Jennifer Linderman, Nikola Banovic and Xun Huan

Project’s principal investigators (left to right): Kathryn Luker (Radiology), Gary Luker (Radiology, Microbiology and Immonology, and Biomedical Engineering), Krishna Garikipati (Mechanical Engineering, and Mathematics), Jennifer Linderman (Chemical Engineering, and Mathematics), Nikola Banovic (Electrical Engineering and Computer Science) and Xun Huan (Mechanical Engineering).

“Get non-Real”: Department of Energy grant funds novel research in High-Performance Algorithms at U-M

By | Feature, Research

“Preparing for the future means that we must continue to invest in the development of next-generation algorithms for scientific computing,

Barbara Helland, Associate Director for Advanced Scientific Computing Research, DOE Office of Science
Source: www.energy.gov/science/articles/department-energy-invests-28-million-novel-research-high-performance-algorithms

New research from the University of Michigan will help revolutionize the data processing pipeline with state-of-the-art algorithms to optimize the collection and processing of any kind of data. Algorithms available now are built for real data, meaning real numbers, however, most of the data we see on the internet is non-real, like discrete data, or categorical. This project is part of a $2.8 million grant from the Department of Energy on algorithms research, which is the backbone of predictive modeling and simulation. The research will enable DOE to set new frontiers in physics, chemistry, biology, and other domains. 

“Preparing for the future means that we must continue to invest in the development of next-generation algorithms for scientific computing,” said Barbara Helland, Associate Director for Advanced Scientific Computing Research, DOE Office of Science. “Foundational research in algorithms is essential for ensuring their efficiency and reliability in meeting the emerging scientific needs of the DOE and the United States.”

The U-M project, led by associate professor Laura Balzano and assistant professor Hessam Mahdavifar, both of electrical engineering and computer science, is one of six chosen by DOE to cover several topics at the leading-edge of algorithms research. According to the DOE, researchers will explore algorithms for analyzing data from biology, energy storage, and other applications. They will develop fast and efficient algorithms as building blocks for tackling increasingly large data analysis problems from scientific measurements, simulations, and experiments. Projects will also address challenges in solving large-scale computational fluid dynamics and related problems.

Laura Balzano and Hessam Mahdavifar portraits

Laura Balzano, associate professor of electrical engineering and computer science (left); Hessam Mahdavifar assistant professor of electrical engineering and computer science (right)

Balzano and Mahdavifar, both Michigan Institute for Computational Discovery and Engineering (MICDE) affiliated faculty members, will use a $300,000 portion of the overall grant to study randomized sketching and compression for high-dimensional non-real-valued data with low-dimensional structures.

“Randomized sketching and subsampling algorithms are revolutionizing the data processing pipeline by allowing significant compression of redundant information,” said Balzano. “Sketches work well because scientific data are generally highly redundant in nature, often following a perturbed low-dimensional structure. Hence, low-rank models and sketching that preserves those model structures are ubiquitous in many machine learning and signal processing applications.” 

Even though a lot of the data used and processed in scientific and technological applications are best modeled mathematically as discrete, categorical or ordinal data, most state-of-the art randomized sketching algorithms focus on real-valued data. To add to this, in practical applications, treating high-dimensional data can be challenging in terms of computational and memory demands. Thus, the proposed project will significantly expand the applicability of randomized sketching.

“A key to data-driven modeling is to carefully reformulate the computational and data analysis challenges and take full advantage of the underlying mathematical structure that is often common across application areas,” said Krishna Garikipati, MICDE director and professor of mechanical engineering and mathematics.”This research and the work that Laura and Hessam are doing is critically important to the advancement of computational discovery.”

Fall 2021 Information Sessions

By | Educational, Events

Fall 2021 information sessions on graduate programs in computational and data sciences at U-M

U-M graduate students interested in computational and data sciences are invited to learn about joint programs that will prepare them for success in computationally intensive fields. The programs are organized by the Michigan Institute for Computational Discovery & Engineering, and the Michigan Institute for Data Science. Both institutes offer vast training and networking opportunities, including webinar series, symposia and student centered events.

Two sessions are scheduled

The sessions will address:

  • The Graduate Certificate in Computational Discovery and Engineering: trains students in computationally intensive research so they can excel in interdisciplinary HPC-focused research and product development environments.

  • The Graduate Certificate in Data Science: focuses on developing core proficiencies in data analytics: modeling, technology and practice.

  • The Graduate Certificate in Computational Neuroscience: provides training in interdisciplinary computational neuroscience to students in experimental neuroscience programs, and to students in quantitative science programs, such as physics, biophysics, mathematics and engineering.

  • The  Ph.D. in Scientific Computing: open to all Ph.D. students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their studies. It is a joint degree program, with students earning a Ph.D. from their current departments, “… and Scientific Computing” — for example, “Ph.D. in Aerospace Engineering and Scientific Computing.”

MICDE catalyst grant leads to new NSF funding to study cascade “ecohydromics” in the Amazonian headwater system

By | Feature, News, Research

The Amazon Basin cycles more water through streamflow and evaporation than any other contiguous forest in the world, and transpiration by trees is a critical part of this cycle. Understanding how plant roots, stems, and leaves interact with soil water to regulate forest transpiration across landscapes is a critical knowledge gap, especially as climate changes. Professor Valeriy Ivanov, from the Department of Civil and Environmental Engineering at U-M, is the lead investigator in a newly NSF funded project that links diverse disciplines – plant ecophysiology, ecology, and hydrology – and will build a unique modeling framework to characterize landscape variation in physiological and hydrological processes in the Amazon Basin. The framework will integrate a wide array of field observations with detailed watershed modeling for hypothesis testing. The team includes Tyeen Taylor, research fellow also from the Civil and Environmental Engineering Department at U-M, and many collaborators in the U.S. at the University of Arizona, University of West Virginia, University of Nebraska, as well as Brazilian researchers at the Federal University of Eastern Para, and Federal University of Amazonas, National Institute for Amazonian Research, and Eastern Amazon Agricultural Agency. Detailed, physical models of ecophysiology and above- and below-ground hydrology will be informed by observations of leaf physiology, tree morphological traits, soil moisture, groundwater, and streamflow. Data and models will be integrated employing novel tools in probabilistic learning and uncertainty quantification. The computational framework tools to be used in this project were developed in part with the support from MICDE Catalyst grant program for the 2018 project “Urban Flood Modeling at “Human Action” Scale: Harnessing the Power of Reduced-Order Approaches and Uncertainty Quantification” led by Prof. Ivanov. 

Given (a) a mechanistic model M (e.g., a stomatal conductance model), (b) one can treat its inputs 𝛏 (e.g., parameters) as random variables. These inputs are sampled and model simulations are carried out. Using (c) PCEs, we construct a surrogate model that best approximates the model output – left-hand-side of (c). The surrogate is then evaluated with Monte Carlo simulations and used for (d) parameter inference. (d.1) is the flow of outputs from the surrogate model into a likelihood function L (D | 𝛏) to compare the surrogate model output and observed data D. This inference produces the posterior distribution for 𝛏. This pdf can then be sent back to the surrogate in (d.2) to reduce the uncertainty in the inputs and to obtain pdf for a quantity of interest (e).

“The reduced-ordered modeling approach developed during the MICDE Catalyst grant project is a key element of the new project,” said Prof. Ivanov, “the MICDE seed funding has allowed us to build a general framework that is applicable to a wide range of computational applications in earth-system science, and thus made our project proposal more competitive”.

The MICDE Catalyst Grants program funds projects that have the potential to catalyze and reorient the directions of their research fields by developing and harnessing powerful paradigms of computational science. This new NSF project is an example of the reach of the program.

Read more.

Prof. Monica Valluri Joins MICDE Leadership Team

By | News, Uncategorized
Portrait of Monica Valluri

Monica Valluri, Astronomy

This month, MICDE welcomed Monica Valluri, Research Professor of Astronomy, as an Associate Director. Prof. Valluri’s research is on the theoretical framework of Galactic Dynamics. Dr. Valluri uses galactic dynamics to interpret and model motions of stars observed with state-of-the-art telescopes using new and powerful numerical methods. Her work has led to important insights into how these dark components influence the structure and evolution of galaxies. In 2016, she won the University of Michigan Research Faculty Achievement Award for her or her outstanding research and teaching career in theoretical galaxy dynamics. In 2019, Prof. Valluri and a team of international collaborators were awarded an MICDE Catalyst Grant, “Determining the 3D Shape of Milky Way’s Dark Matter Halo” that has lead to several federally funded grants.

Annette Ostling - Portrait

Annette Ostling, Ecology and Evolutionary Biology

Prof. Valluri’s appointment succeeds former Associate Director Annette Ostling, Associate Professor of Ecology and Evolutionary Biology. We thank Prof. Ostling for her service to MICDE. Her contributions to the growth and resiliency of MICDE have been numerous over the last five years. 

miRcore is looking for group leaders to guide high school students in performing computational biomedical research

By | Feature, SC2 jobs

miRcore, a 501(c)(3) non-profit org., is looking for group leaders to guide groups of high school students in performing computational biomedical research. Group leaders will be paired with a younger co-lead. During our remote camps, students will finish a research project within about one week, while learning and applying new tools every day. Group leaders will be expected to assist and support these students.

2017 miRcore GIDAS Biotechnology Summer Camp participants

Ideal group leaders are:
1) Graduate or college students (professionals with PhDs welcome)
2) Experienced (or interested) in computational biomedical research
3) Have a passion to inspire young minds
4) Able to commit to a specific camp

Camps run June-August, and leaders will be compensated with $450 per camp session.
Group leaders usually describe the experience as fun, meaningful, an opportunity to learn new research skills, and reigniting their passion for science.

Learn more and apply here: https://forms.gle/6oD9cDqULebxarCE7